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Classification of Multiple Diseases on Body CT Scans Using Weakly Supervised Deep Learning.

Publication ,  Journal Article
Tushar, FI; D'Anniballe, VM; Hou, R; Mazurowski, MA; Fu, W; Samei, E; Rubin, GD; Lo, JY
Published in: Radiol Artif Intell
January 2022

PURPOSE: To design multidisease classifiers for body CT scans for three different organ systems using automatically extracted labels from radiology text reports. MATERIALS AND METHODS: This retrospective study included a total of 12 092 patients (mean age, 57 years ± 18 [standard deviation]; 6172 women) for model development and testing. Rule-based algorithms were used to extract 19 225 disease labels from 13 667 body CT scans performed between 2012 and 2017. Using a three-dimensional DenseVNet, three organ systems were segmented: lungs and pleura, liver and gallbladder, and kidneys and ureters. For each organ system, a three-dimensional convolutional neural network classified each as no apparent disease or for the presence of four common diseases, for a total of 15 different labels across all three models. Testing was performed on a subset of 2158 CT volumes relative to 2875 manually derived reference labels from 2133 patients (mean age, 58 years ± 18; 1079 women). Performance was reported as area under the receiver operating characteristic curve (AUC), with 95% CIs calculated using the DeLong method. RESULTS: Manual validation of the extracted labels confirmed 91%-99% accuracy across the 15 different labels. AUCs for lungs and pleura labels were as follows: atelectasis, 0.77 (95% CI: 0.74, 0.81); nodule, 0.65 (95% CI: 0.61, 0.69); emphysema, 0.89 (95% CI: 0.86, 0.92); effusion, 0.97 (95% CI: 0.96, 0.98); and no apparent disease, 0.89 (95% CI: 0.87, 0.91). AUCs for liver and gallbladder were as follows: hepatobiliary calcification, 0.62 (95% CI: 0.56, 0.67); lesion, 0.73 (95% CI: 0.69, 0.77); dilation, 0.87 (95% CI: 0.84, 0.90); fatty, 0.89 (95% CI: 0.86, 0.92); and no apparent disease, 0.82 (95% CI: 0.78, 0.85). AUCs for kidneys and ureters were as follows: stone, 0.83 (95% CI: 0.79, 0.87); atrophy, 0.92 (95% CI: 0.89, 0.94); lesion, 0.68 (95% CI: 0.64, 0.72); cyst, 0.70 (95% CI: 0.66, 0.73); and no apparent disease, 0.79 (95% CI: 0.75, 0.83). CONCLUSION: Weakly supervised deep learning models were able to classify diverse diseases in multiple organ systems from CT scans.Keywords: CT, Diagnosis/Classification/Application Domain, Semisupervised Learning, Whole-Body Imaging© RSNA, 2022.

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Published In

Radiol Artif Intell

DOI

EISSN

2638-6100

Publication Date

January 2022

Volume

4

Issue

1

Start / End Page

e210026

Location

United States
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Tushar, F. I., D’Anniballe, V. M., Hou, R., Mazurowski, M. A., Fu, W., Samei, E., … Lo, J. Y. (2022). Classification of Multiple Diseases on Body CT Scans Using Weakly Supervised Deep Learning. Radiol Artif Intell, 4(1), e210026. https://doi.org/10.1148/ryai.210026
Tushar, Fakrul Islam, Vincent M. D’Anniballe, Rui Hou, Maciej A. Mazurowski, Wanyi Fu, Ehsan Samei, Geoffrey D. Rubin, and Joseph Y. Lo. “Classification of Multiple Diseases on Body CT Scans Using Weakly Supervised Deep Learning.Radiol Artif Intell 4, no. 1 (January 2022): e210026. https://doi.org/10.1148/ryai.210026.
Tushar FI, D’Anniballe VM, Hou R, Mazurowski MA, Fu W, Samei E, et al. Classification of Multiple Diseases on Body CT Scans Using Weakly Supervised Deep Learning. Radiol Artif Intell. 2022 Jan;4(1):e210026.
Tushar, Fakrul Islam, et al. “Classification of Multiple Diseases on Body CT Scans Using Weakly Supervised Deep Learning.Radiol Artif Intell, vol. 4, no. 1, Jan. 2022, p. e210026. Pubmed, doi:10.1148/ryai.210026.
Tushar FI, D’Anniballe VM, Hou R, Mazurowski MA, Fu W, Samei E, Rubin GD, Lo JY. Classification of Multiple Diseases on Body CT Scans Using Weakly Supervised Deep Learning. Radiol Artif Intell. 2022 Jan;4(1):e210026.

Published In

Radiol Artif Intell

DOI

EISSN

2638-6100

Publication Date

January 2022

Volume

4

Issue

1

Start / End Page

e210026

Location

United States